MultiQC_coffee_coassembly: MultiQC Report

General Statistics

Showing ¹³⁶/₁₃₆ rows and ¹⁶/₂₉ columns.

Sample Name	% Dups (raw)	% GC (raw)	Avg. length (raw)	Median length (raw)	M Seqs (raw)	% Fails (raw)	% Dups (processed)	% GC (processed)	Avg. length (processed)	Median length (processed)	M Seqs (processed)	% Fails (processed)	% Duplication	% > Q30	Mb Q30 bases	Reads After Filtering	GC content	% PF	% Adapter	% Aligned (Host)	% Aligned (Assem.)	N50 (Kbp)	N50 (Kbp)	Assembly Length (Mbp)	Assembly Length (Mbp)	Contigs	Bases	CDS	Organism
C1_run0													0.5%	100.0%	3619.3Mb	24.1M	41.2%	100.0%	0.7%	0.2%
C1_run0_raw_1	23.4%	41.0%	150bp	150bp	12.1M	0%
C1_run0_raw_2	24.2%	41.0%	150bp	150bp	12.1M	0%
C1_run0_trimmed_1							23.4%	41.0%	150bp	150bp	12.1M	0%
C1_run0_trimmed_2							24.2%	41.0%	150bp	150bp	12.1M	0%
C2_run0													0.5%	100.0%	3621.1Mb	24.1M	41.2%	100.0%	0.3%	0.1%
C2_run0_raw_1	31.8%	41.0%	150bp	150bp	12.1M	0%
C2_run0_raw_2	32.2%	41.0%	150bp	150bp	12.1M	0%
C2_run0_trimmed_1							31.7%	41.0%	150bp	150bp	12.1M	0%
C2_run0_trimmed_2							32.2%	41.0%	150bp	150bp	12.1M	0%
C3_run0													0.4%	100.0%	3642.5Mb	24.3M	49.5%	100.0%	0.3%	0.1%
C3_run0_raw_1	16.9%	49.0%	150bp	150bp	12.1M	0%
C3_run0_raw_2	16.9%	49.0%	150bp	150bp	12.1M	0%
C3_run0_trimmed_1							16.9%	49.0%	150bp	150bp	12.1M	0%
C3_run0_trimmed_2							16.9%	49.0%	150bp	150bp	12.1M	0%
C4_run0													0.5%	100.0%	3600.3Mb	24.0M	46.8%	100.0%	0.6%	0.2%
C4_run0_raw_1	17.5%	46.0%	150bp	150bp	12.0M	0%
C4_run0_raw_2	17.7%	46.0%	150bp	150bp	12.0M	0%
C4_run0_trimmed_1							17.4%	46.0%	150bp	150bp	12.0M	0%
C4_run0_trimmed_2							17.6%	46.0%	150bp	150bp	12.0M	0%
MEGAHIT-CONCOCT-group-0_0																							2.3Kbp		1.5Mbp	666	1475262	1401	Genus species
MEGAHIT-CONCOCT-group-0_1																							1.0Kbp		0.0Mbp	1	1043		Genus species
MEGAHIT-CONCOCT-group-0_2																							1.3Kbp		0.0Mbp	2	2338		Genus species
MEGAHIT-CONCOCT-group-0_3																							1.0Kbp		0.0Mbp	1	1019	1	Genus species
MEGAHIT-CONCOCT-group-0_4																							8.4Kbp		9.1Mbp	1209	9108804	5250	Genus species
MEGAHIT-CONCOCT-group-0_5																							33.1Kbp		0.1Mbp	3	64778	74	Genus species
MEGAHIT-CONCOCT-group-0_6																							1.1Kbp		0.0Mbp	1	1085	1	Genus species
MEGAHIT-CONCOCT-group-0_7																							1.4Kbp		0.0Mbp	5	6740	9	Genus species
MEGAHIT-CONCOCT-group-0_8																							398.7Kbp		9.1Mbp
MEGAHIT-CONCOCT-group-0_9																							58.8Kbp		0.9Mbp
MEGAHIT-CONCOCT-group-0_10																							4.4Kbp		0.0Mbp	1	4430	1	Genus species
MEGAHIT-CONCOCT-group-0_11																							1.9Kbp		0.0Mbp	1	1933	5	Genus species
MEGAHIT-CONCOCT-group-0_12																							1.0Kbp		0.0Mbp	3	3113	1	Genus species
MEGAHIT-CONCOCT-group-0_13																							1.6Kbp		0.0Mbp	5	7552	9	Genus species
MEGAHIT-CONCOCT-group-0_14																							2.5Kbp		0.0Mbp	1	2481	1	Genus species
MEGAHIT-CONCOCT-group-0_15																							83.2Kbp		3.2Mbp	74	3249113	3097	Genus species
MEGAHIT-CONCOCT-group-0_16																							1.2Kbp		0.0Mbp	1	1207	1	Genus species
MEGAHIT-CONCOCT-group-0_17																							21.4Kbp		0.1Mbp	10	119094	61	Genus species
MEGAHIT-CONCOCT-group-0_18																							1.0Kbp		0.0Mbp	1	1038	1	Genus species
MEGAHIT-CONCOCT-group-0_19																							1.3Kbp		0.0Mbp	8	10301	6	Genus species
MEGAHIT-CONCOCT-group-0_20																							2.5Kbp		9.4Mbp	4155	9384874	6354	Genus species
MEGAHIT-CONCOCT-group-0_21																							1.2Kbp		0.0Mbp	18	23545	23	Genus species
MEGAHIT-CONCOCT-group-0_24																							1.3Kbp		0.0Mbp	11	16158	17	Genus species
MEGAHIT-CONCOCT-group-0_25																							1.4Kbp		4.7Mbp	3320	4745041	3218	Genus species
MEGAHIT-CONCOCT-group-0_26																							2.6Kbp		1.1Mbp	527	1134420	715	Genus species
MEGAHIT-CONCOCT-group-0_27																							6.4Kbp		3.6Mbp	776	3590283	3567	Genus species
MEGAHIT-CONCOCT-group-0_28																							20.2Kbp		0.1Mbp	3	66601	38	Genus species
MEGAHIT-CONCOCT-group-0_29																							279.1Kbp		4.8Mbp	43	4767684	4528	Genus species
MEGAHIT-CONCOCT-group-0_30																							1.7Kbp		0.3Mbp	176	298511	284	Genus species
MEGAHIT-CONCOCT-group-0_32																							30.6Kbp		5.0Mbp	257	4992212	5081	Genus species
MEGAHIT-CONCOCT-group-0_33																							145.1Kbp		5.5Mbp	75	5548050	5163	Genus species
MEGAHIT-CONCOCT-group-0_34																							8.3Kbp		0.0Mbp	7	31842	15	Genus species
MEGAHIT-CONCOCT-group-0_35																							1.3Kbp		3.4Mbp	2519	3408983	3164	Genus species
MEGAHIT-CONCOCT-group-0_36																							1.8Kbp		0.0Mbp	1	1801	3	Genus species
MEGAHIT-CONCOCT-group-0_37																							2.1Kbp		0.0Mbp	1	2067	2	Genus species
MEGAHIT-CONCOCT-group-0_38																							136.7Kbp		0.7Mbp	11	735868	661	Genus species
MEGAHIT-CONCOCT-group-0_39																							2.3Kbp		0.0Mbp	2	3372	3	Genus species
MEGAHIT-CONCOCT-group-0_40																							2.0Kbp		1.1Mbp	574	1103225	995	Genus species
MEGAHIT-CONCOCT-group-0_41																							3.0Kbp		0.0Mbp	1	3047	5	Genus species
MEGAHIT-CONCOCT-group-0_42																							117.9Kbp		0.5Mbp
MEGAHIT-CONCOCT-group-0_43																							1.4Kbp		0.8Mbp
MEGAHIT-CONCOCT-group-0_44																							1.6Kbp		0.0Mbp	1	1617	2	Genus species
MEGAHIT-CONCOCT-group-0_45																							1.1Kbp		0.0Mbp	1	1142	1	Genus species
MEGAHIT-CONCOCT-group-0_46																							4.8Kbp		0.3Mbp
MEGAHIT-CONCOCT-group-0_47																							1.6Kbp		0.0Mbp	2	2901	7	Genus species
MEGAHIT-CONCOCT-group-0_48																							1.4Kbp		0.0Mbp	1	1443	2	Genus species
MEGAHIT-CONCOCT-group-0_49																							2.0Kbp		0.1Mbp	49	92031	43	Genus species
MEGAHIT-CONCOCT-group-0_50																							31.7Kbp		0.0Mbp	1	31735	15	Genus species
MEGAHIT-CONCOCT-group-0_51																							1.5Kbp		0.0Mbp	2	2658	1	Genus species
MEGAHIT-CONCOCT-group-0_52																							7.6Kbp		0.5Mbp	108	539762	577	Genus species
MEGAHIT-CONCOCT-group-0_53																							4.2Kbp		0.2Mbp	57	176572	124	Genus species
MEGAHIT-CONCOCT-group-0_54																							1.1Kbp		0.0Mbp	1	1055	2	Genus species
MEGAHIT-CONCOCT-group-0_55																							1.2Kbp		0.3Mbp
MEGAHIT-CONCOCT-group-0_56																							69.8Kbp		2.6Mbp	134	2626115	2740	Genus species
MEGAHIT-CONCOCT-group-0_57																							134.9Kbp		4.8Mbp
MEGAHIT-CONCOCT-group-0_58																							2.3Kbp		0.0Mbp	14	29303	18	Genus species
MEGAHIT-CONCOCT-group-0_59																							1.3Kbp		0.0Mbp	2	2499	3	Genus species
MEGAHIT-CONCOCT-group-0_60																							2.2Kbp		0.0Mbp	12	23977	27	Genus species
MEGAHIT-CONCOCT-group-0_61																							1.3Kbp		0.0Mbp	1	1282	1	Genus species
MEGAHIT-MaxBin2-group-0.001																							11.2Kbp		0.1Mbp	18	101756	99	Genus species
MEGAHIT-MaxBin2-group-0.002																							83.2Kbp		3.2Mbp	75	3248562	3106	Genus species
MEGAHIT-MaxBin2-group-0.006																							22.2Kbp		6.9Mbp	1083	6852909	6882	Genus species
MEGAHIT-MaxBin2-group-0.010																							279.1Kbp		4.8Mbp	53	4824611	4575	Genus species
MEGAHIT-MaxBin2-group-0.011																							6.5Kbp		3.4Mbp	730	3429168	3399	Genus species
MEGAHIT-MetaBAT2-group-0.1																							88.0Kbp		0.4Mbp
MEGAHIT-MetaBAT2-group-0.3																							6.7Kbp		3.4Mbp	645	3418393	3402	Genus species
MEGAHIT-MetaBAT2-group-0.4																							92.0Kbp		2.0Mbp	41	1968209	1986	Genus species
MEGAHIT-MetaBAT2-group-0.5																							398.7Kbp		9.2Mbp
MEGAHIT-MetaBAT2-group-0.7																							114.2Kbp		0.3Mbp
MEGAHIT-MetaBAT2-group-0.9																							3.4Kbp		0.8Mbp	253	825509	712	Genus species
MEGAHIT-MetaBAT2-group-0.11																							30.6Kbp		5.3Mbp	315	5337488	5402	Genus species
MEGAHIT-MetaBAT2-group-0.12																							2.9Kbp		0.3Mbp	108	313842	344	Genus species
MEGAHIT-MetaBAT2-group-0.13																							80.0Kbp		3.1Mbp	82	3127881	2958	Genus species
MEGAHIT-MetaBAT2-group-0.14																							69.3Kbp		0.2Mbp
MEGAHIT-MetaBAT2-group-0.15																							194.9Kbp		0.2Mbp
MEGAHIT-MetaBAT2-group-0.16																							1.9Kbp		2.1Mbp
MEGAHIT-MetaBAT2-group-0.17																							166.6Kbp		0.3Mbp
MEGAHIT-MetaBAT2-group-0.18																							431.2Kbp		1.4Mbp
MEGAHIT-MetaBAT2-group-0.19																							151.5Kbp		5.3Mbp	55	5273130	4878	Genus species
MEGAHIT-MetaBAT2-group-0.20																							176.2Kbp		3.5Mbp
MEGAHIT-MetaBAT2-group-0.21																							279.1Kbp		4.8Mbp	41	4758122	4491	Genus species
MEGAHIT-MetaBAT2-group-0.23																							50.4Kbp		0.3Mbp
MEGAHIT-MetaBAT2-group-0.24																							9.3Kbp		0.2Mbp	31	202088	224	Genus species
MEGAHIT-MetaBAT2-group-0.25																							82.4Kbp		1.2Mbp
MEGAHIT-MetaBAT2-group-0.26																							614.3Kbp		0.6Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.1																							186.2Kbp		0.2Mbp	1	186216	172	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.2																							90.2Kbp		0.1Mbp	1	90198	80	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.3																							67.1Kbp		0.1Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.4																							66.7Kbp		0.1Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.5																							58.8Kbp		0.1Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.6																							40.9Kbp		0.0Mbp	1	40871	49	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.7																							33.1Kbp		0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.8																							31.8Kbp		0.0Mbp	1	31832	32	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.9																							31.8Kbp		0.0Mbp	1	31765	38	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.10																							31.7Kbp		0.0Mbp	1	31735	15	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.11																							31.6Kbp		0.0Mbp	1	31555	26	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.12																							30.6Kbp		0.0Mbp	1	30557	15	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.13																							30.0Kbp		0.0Mbp	1	29978	35	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.14																							29.5Kbp		0.0Mbp	1	29492	22	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.15																							29.2Kbp		0.0Mbp	1	29161	35	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.16																							27.0Kbp		0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.17																							26.4Kbp		0.0Mbp	1	26409	46	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.18																							25.1Kbp		0.0Mbp	1	25144	21	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.19																							24.6Kbp		0.0Mbp	1	24615	25	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.20																							24.0Kbp		0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.21																							23.8Kbp		0.0Mbp	1	23832	23	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.22																							23.5Kbp		0.0Mbp	1	23509	19	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.23																							21.5Kbp		0.0Mbp	1	21469	14	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.24																							21.5Kbp		0.0Mbp	1	21465	12	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.25																							21.4Kbp		0.0Mbp	1	21365	6	Genus species
MEGAHIT-MetaBAT2-group-0.unbinned.26																							20.2Kbp		0.0Mbp	1	20155	7	Genus species
MEGAHIT-group-0																						25.7Kbp		194.1Mbp
MEGAHIT-group-0-C1																					96.9%
MEGAHIT-group-0-C2																					98.7%
MEGAHIT-group-0-C3																					99.3%
MEGAHIT-group-0-C4																					98.6%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: general_stats_table_table

Sort	Group	Column	Description	ID	Scale
\|\|	FastQC: raw reads	% Dups (raw)	% duplicate reads	`fastqc_raw_reads-percent_duplicates`
\|\|	FastQC: raw reads	% GC (raw)	Average % GC content	`fastqc_raw_reads-percent_gc`
\|\|	FastQC: raw reads	Avg. length (raw)	Average read length	`fastqc_raw_reads-avg_sequence_length`
\|\|	FastQC: raw reads	Median length (raw)	Median read length	`fastqc_raw_reads-median_sequence_length`
\|\|	FastQC: raw reads	M Seqs (raw)	Total sequences (millions)	`fastqc_raw_reads-total_sequences`	read_count
\|\|	FastQC: raw reads	% Fails (raw)	Percentage of modules failed in FastQC report (includes those not plotted here)	`fastqc_raw_reads-percent_fails`
\|\|	FastQC: after preprocessing	% Dups (processed)	% duplicate reads	`fastqc_after_preprocessing-percent_duplicates`
\|\|	FastQC: after preprocessing	% GC (processed)	Average % GC content	`fastqc_after_preprocessing-percent_gc`
\|\|	FastQC: after preprocessing	Avg. length (processed)	Average read length	`fastqc_after_preprocessing-avg_sequence_length`
\|\|	FastQC: after preprocessing	Median length (processed)	Median read length	`fastqc_after_preprocessing-median_sequence_length`
\|\|	FastQC: after preprocessing	M Seqs (processed)	Total sequences (millions)	`fastqc_after_preprocessing-total_sequences`	read_count
\|\|	FastQC: after preprocessing	% Fails (processed)	Percentage of modules failed in FastQC report (includes those not plotted here)	`fastqc_after_preprocessing-percent_fails`
\|\|	fastp	% Duplication	Duplication rate before filtering	`fastp-pct_duplication`
\|\|	fastp	% > Q30	Percentage of reads > Q30 after filtering	`fastp-after_filtering_q30_rate`
\|\|	fastp	Mb Q30 bases	Bases > Q30 after filtering (millions)	`fastp-after_filtering_q30_bases`	base_count
\|\|	fastp	Reads After Filtering	Total reads after filtering (millions)	`fastp-filtering_result_passed_filter_reads`	read_count
\|\|	fastp	GC content	GC content after filtering	`fastp-after_filtering_gc_content`
\|\|	fastp	% PF	Percent reads passing filter	`fastp-pct_surviving`
\|\|	fastp	% Adapter	Percentage adapter-trimmed reads	`fastp-pct_adapter`
\|\|	Bowtie2: host removal	% Aligned (Host)	overall alignment rate	`bowtie2_host_removal-overall_alignment_rate`
\|\|	Bowtie2: assembly	% Aligned (Assem.)	overall alignment rate	`bowtie2_assembly-overall_alignment_rate`
\|\|	QUAST: assembly	N50 (Kbp)	N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly (kilo base pairs)	`quast_assembly-N50`
\|\|	QUAST: bins	N50 (Kbp)	N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly (kilo base pairs)	`quast_bins-N50`
\|\|	QUAST: assembly	Assembly Length (Mbp)	The total number of bases in the assembly (Mbp).	`quast_assembly-Total_length`
\|\|	QUAST: bins	Assembly Length (Mbp)	The total number of bases in the assembly (Mbp).	`quast_bins-Total_length`
\|\|	Prokka	Contigs	Number of contigs	`prokka-contigs`
\|\|	Prokka	Bases	Number of bases	`prokka-bases`
\|\|	Prokka	CDS	Number of CDS	`prokka-CDS`
\|\|	Prokka	Organism	Organism	`prokka-organism`

FastQC: raw reads

Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Created with MultiQC

Sequence Quality Histograms

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

Created with MultiQC

Per Sequence Quality Scores

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

Created with MultiQC

Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

Per Sequence GC Content

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Created with MultiQC

Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Created with MultiQC

Sequence Length Distribution

All samples have sequences of a single length (150bp)

Sequence Duplication Levels

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Created with MultiQC

Overrepresented sequences by sample

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

8 samples had less than 1% of reads made up of overrepresented sequences

Adapter Content

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

Created with MultiQC

fastp

All-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).URL: https://github.com/OpenGene/fastpDOI: 10.1093/bioinformatics/bty560

Fastp goes through fastq files in a folder and perform a series of quality control and filtering. Quality control and reporting are displayed both before and after filtering, allowing for a clear depiction of the consequences of the filtering process. Notably, the latter can be conducted on a variety of parameters including quality scores, length, as well as the presence of adapters, polyG, or polyX tailing.

Filtered Reads

Filtering statistics of sampled reads.

Created with MultiQC

Insert Sizes

Insert size estimation of sampled reads.

Created with MultiQC

Sequence Quality

Average sequencing quality over each base of all reads.

Created with MultiQC

GC Content

Average GC content over each base of all reads.

Created with MultiQC

N content

Average N content over each base of all reads.

Created with MultiQC

FastQC: after preprocessing

After trimming and, if requested, contamination removal.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Created with MultiQC

Sequence Quality Histograms

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

Created with MultiQC

Per Sequence Quality Scores

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

Created with MultiQC

Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

Per Sequence GC Content

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Created with MultiQC

Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Created with MultiQC

Sequence Length Distribution

The distribution of fragment sizes (read lengths) found. See the FastQC help

Created with MultiQC

Sequence Duplication Levels

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Created with MultiQC

Overrepresented sequences by sample

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

8 samples had less than 1% of reads made up of overrepresented sequences

Adapter Content

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

Created with MultiQC

Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

Created with MultiQC

Bowtie2: host removal

Mapping statistics of reads mapped against host genome and subsequently removed.URL: http://bowtie-bio.sourceforge.net/bowtie2; https://ccb.jhu.edu/software/hisat2DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4

Paired-end alignments

This plot shows the number of reads aligning to the reference in different ways.

Please note that single mate alignment counts are halved to tally with pair counts properly.

There are 6 possible types of alignment:

PE mapped uniquely: Pair has only one occurence in the reference genome.
PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
PE one mate mapped uniquely: One read of a pair has one occurence.
PE multimapped: Pair has multiple occurence.
PE one mate multimapped: One read of a pair has multiple occurence.
PE neither mate aligned: Pair has no occurence.

Created with MultiQC

QUAST: assembly

Assembly statistics of raw assemblies.URL: http://quast.bioinf.spbau.ruDOI: 10.1093/bioinformatics/btt086

Assembly Statistics

Showing ¹/₁ rows and ⁶/₆ columns.

Sample Name	N50 (Kbp)	N75 (Kbp)	L50 (K)	L75 (K)	Largest contig (Kbp)	Length (Mbp)
MEGAHIT-group-0	25.7Kbp	1.9Kbp	1.0K	11.6K	1765.0Kbp	194.1Mbp

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: quast_table_table

Sort	Column	Description	ID
\|\|	N50 (Kbp)	N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly.	`quast-N50`
\|\|	N75 (Kbp)	N75 is the contig length such that using longer or equal length contigs produces 75% of the bases of the assembly	`quast-N75`
\|\|	L50 (K)	L50 is the number of contigs larger than N50, i.e. the minimum number of contigs comprising 50% of the total assembly length.	`quast-L50`
\|\|	L75 (K)	L75 is the number of contigs larger than N75, i.e. the minimum number of contigs comprising 75% of the total assembly length.	`quast-L75`
\|\|	Largest contig (Kbp)	The size of the largest contig of the assembly	`quast-Largest_contig`
\|\|	Length (Mbp)	The total number of bases in the assembly.	`quast-Total_length`

Number of Contigs

This plot shows the number of contigs found for each assembly, broken down by length.

Created with MultiQC

Bowtie2: assembly

Mapping statistics of reads mapped against assemblies.URL: http://bowtie-bio.sourceforge.net/bowtie2; https://ccb.jhu.edu/software/hisat2DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4

Paired-end alignments

This plot shows the number of reads aligning to the reference in different ways.

Please note that single mate alignment counts are halved to tally with pair counts properly.

There are 6 possible types of alignment:

PE mapped uniquely: Pair has only one occurence in the reference genome.
PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
PE one mate mapped uniquely: One read of a pair has one occurence.
PE multimapped: Pair has multiple occurence.
PE one mate multimapped: One read of a pair has multiple occurence.
PE neither mate aligned: Pair has no occurence.

Created with MultiQC

QUAST: bins

Assembly statistics of binned assemblies.URL: http://quast.bioinf.spbau.ruDOI: 10.1093/bioinformatics/btt086

Assembly Statistics

Showing ¹¹¹/₁₁₁ rows and ⁶/₆ columns.

Sample Name	N50 (Kbp)	N75 (Kbp)	L50 (K)	L75 (K)	Largest contig (Kbp)	Length (Mbp)
MEGAHIT-CONCOCT-group-0_0	2.3Kbp	1.4Kbp	0.1K	0.3K	69.9Kbp	1.5Mbp
MEGAHIT-CONCOCT-group-0_1	1.0Kbp	1.0Kbp	0.0K	0.0K	1.0Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_2	1.3Kbp	1.0Kbp	0.0K	0.0K	1.3Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_3	1.0Kbp	1.0Kbp	0.0K	0.0K	1.0Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_4	8.4Kbp	5.9Kbp	0.4K	0.7K	41.7Kbp	9.1Mbp
MEGAHIT-CONCOCT-group-0_5	33.1Kbp	28.9Kbp	0.0K	0.0K	33.1Kbp	0.1Mbp
MEGAHIT-CONCOCT-group-0_6	1.1Kbp	1.1Kbp	0.0K	0.0K	1.1Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_7	1.4Kbp	1.1Kbp	0.0K	0.0K	1.8Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_8	398.7Kbp	142.7Kbp	0.0K	0.0K	929.4Kbp	9.1Mbp
MEGAHIT-CONCOCT-group-0_9	58.8Kbp	27.0Kbp	0.0K	0.0K	194.9Kbp	0.9Mbp
MEGAHIT-CONCOCT-group-0_10	4.4Kbp	4.4Kbp	0.0K	0.0K	4.4Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_11	1.9Kbp	1.9Kbp	0.0K	0.0K	1.9Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_12	1.0Kbp	1.0Kbp	0.0K	0.0K	1.1Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_13	1.6Kbp	1.5Kbp	0.0K	0.0K	1.7Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_14	2.5Kbp	2.5Kbp	0.0K	0.0K	2.5Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_15	83.2Kbp	48.4Kbp	0.0K	0.0K	186.2Kbp	3.2Mbp
MEGAHIT-CONCOCT-group-0_16	1.2Kbp	1.2Kbp	0.0K	0.0K	1.2Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_17	21.4Kbp	10.4Kbp	0.0K	0.0K	22.8Kbp	0.1Mbp
MEGAHIT-CONCOCT-group-0_18	1.0Kbp	1.0Kbp	0.0K	0.0K	1.0Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_19	1.3Kbp	1.2Kbp	0.0K	0.0K	1.5Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_20	2.5Kbp	1.7Kbp	1.3K	2.4K	23.9Kbp	9.4Mbp
MEGAHIT-CONCOCT-group-0_21	1.2Kbp	1.1Kbp	0.0K	0.0K	2.8Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_24	1.3Kbp	1.1Kbp	0.0K	0.0K	2.3Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_25	1.4Kbp	1.2Kbp	1.3K	2.2K	5.6Kbp	4.7Mbp
MEGAHIT-CONCOCT-group-0_26	2.6Kbp	1.3Kbp	0.1K	0.3K	30.6Kbp	1.1Mbp
MEGAHIT-CONCOCT-group-0_27	6.4Kbp	3.7Kbp	0.2K	0.3K	36.3Kbp	3.6Mbp
MEGAHIT-CONCOCT-group-0_28	20.2Kbp	17.0Kbp	0.0K	0.0K	29.5Kbp	0.1Mbp
MEGAHIT-CONCOCT-group-0_29	279.1Kbp	108.3Kbp	0.0K	0.0K	633.9Kbp	4.8Mbp
MEGAHIT-CONCOCT-group-0_30	1.7Kbp	1.3Kbp	0.1K	0.1K	6.7Kbp	0.3Mbp
MEGAHIT-CONCOCT-group-0_32	30.6Kbp	15.9Kbp	0.0K	0.1K	146.8Kbp	5.0Mbp
MEGAHIT-CONCOCT-group-0_33	145.1Kbp	90.8Kbp	0.0K	0.0K	306.6Kbp	5.5Mbp
MEGAHIT-CONCOCT-group-0_34	8.3Kbp	3.5Kbp	0.0K	0.0K	10.3Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_35	1.3Kbp	1.1Kbp	1.0K	1.7K	3.6Kbp	3.4Mbp
MEGAHIT-CONCOCT-group-0_36	1.8Kbp	1.8Kbp	0.0K	0.0K	1.8Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_37	2.1Kbp	2.1Kbp	0.0K	0.0K	2.1Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_38	136.7Kbp	59.9Kbp	0.0K	0.0K	283.3Kbp	0.7Mbp
MEGAHIT-CONCOCT-group-0_39	2.3Kbp	1.0Kbp	0.0K	0.0K	2.3Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_40	2.0Kbp	1.4Kbp	0.2K	0.3K	15.4Kbp	1.1Mbp
MEGAHIT-CONCOCT-group-0_41	3.0Kbp	3.0Kbp	0.0K	0.0K	3.0Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_42	117.9Kbp	22.7Kbp	0.0K	0.0K	138.2Kbp	0.5Mbp
MEGAHIT-CONCOCT-group-0_43	1.4Kbp	1.2Kbp	0.2K	0.4K	5.7Kbp	0.8Mbp
MEGAHIT-CONCOCT-group-0_44	1.6Kbp	1.6Kbp	0.0K	0.0K	1.6Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_45	1.1Kbp	1.1Kbp	0.0K	0.0K	1.1Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_46	4.8Kbp	2.7Kbp	0.0K	0.0K	20.5Kbp	0.3Mbp
MEGAHIT-CONCOCT-group-0_47	1.6Kbp	1.3Kbp	0.0K	0.0K	1.6Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_48	1.4Kbp	1.4Kbp	0.0K	0.0K	1.4Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_49	2.0Kbp	1.2Kbp	0.0K	0.0K	7.5Kbp	0.1Mbp
MEGAHIT-CONCOCT-group-0_50	31.7Kbp	31.7Kbp	0.0K	0.0K	31.7Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_51	1.5Kbp	1.1Kbp	0.0K	0.0K	1.5Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_52	7.6Kbp	3.5Kbp	0.0K	0.0K	31.6Kbp	0.5Mbp
MEGAHIT-CONCOCT-group-0_53	4.2Kbp	2.2Kbp	0.0K	0.0K	28.9Kbp	0.2Mbp
MEGAHIT-CONCOCT-group-0_54	1.1Kbp	1.1Kbp	0.0K	0.0K	1.1Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_55	1.2Kbp	1.1Kbp	0.1K	0.1K	7.5Kbp	0.3Mbp
MEGAHIT-CONCOCT-group-0_56	69.8Kbp	19.8Kbp	0.0K	0.0K	489.8Kbp	2.6Mbp
MEGAHIT-CONCOCT-group-0_57	134.9Kbp	50.8Kbp	0.0K	0.0K	270.0Kbp	4.8Mbp
MEGAHIT-CONCOCT-group-0_58	2.3Kbp	1.8Kbp	0.0K	0.0K	4.2Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_59	1.3Kbp	1.2Kbp	0.0K	0.0K	1.3Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_60	2.2Kbp	1.5Kbp	0.0K	0.0K	4.3Kbp	0.0Mbp
MEGAHIT-CONCOCT-group-0_61	1.3Kbp	1.3Kbp	0.0K	0.0K	1.3Kbp	0.0Mbp
MEGAHIT-MaxBin2-group-0.001	11.2Kbp	3.5Kbp	0.0K	0.0K	33.1Kbp	0.1Mbp
MEGAHIT-MaxBin2-group-0.002	83.2Kbp	48.4Kbp	0.0K	0.0K	186.2Kbp	3.2Mbp
MEGAHIT-MaxBin2-group-0.006	22.2Kbp	6.5Kbp	0.1K	0.2K	146.8Kbp	6.9Mbp
MEGAHIT-MaxBin2-group-0.010	279.1Kbp	108.3Kbp	0.0K	0.0K	633.9Kbp	4.8Mbp
MEGAHIT-MaxBin2-group-0.011	6.5Kbp	3.8Kbp	0.2K	0.3K	28.3Kbp	3.4Mbp
MEGAHIT-MetaBAT2-group-0.1	88.0Kbp	46.0Kbp	0.0K	0.0K	134.9Kbp	0.4Mbp
MEGAHIT-MetaBAT2-group-0.3	6.7Kbp	4.0Kbp	0.1K	0.3K	36.3Kbp	3.4Mbp
MEGAHIT-MetaBAT2-group-0.4	92.0Kbp	47.0Kbp	0.0K	0.0K	489.8Kbp	2.0Mbp
MEGAHIT-MetaBAT2-group-0.5	398.7Kbp	142.7Kbp	0.0K	0.0K	929.4Kbp	9.2Mbp
MEGAHIT-MetaBAT2-group-0.7	114.2Kbp	114.2Kbp	0.0K	0.0K	117.9Kbp	0.3Mbp
MEGAHIT-MetaBAT2-group-0.9	3.4Kbp	2.2Kbp	0.1K	0.1K	21.2Kbp	0.8Mbp
MEGAHIT-MetaBAT2-group-0.11	30.6Kbp	15.5Kbp	0.0K	0.1K	146.8Kbp	5.3Mbp
MEGAHIT-MetaBAT2-group-0.12	2.9Kbp	2.0Kbp	0.0K	0.1K	10.3Kbp	0.3Mbp
MEGAHIT-MetaBAT2-group-0.13	80.0Kbp	47.2Kbp	0.0K	0.0K	184.2Kbp	3.1Mbp
MEGAHIT-MetaBAT2-group-0.14	69.3Kbp	69.3Kbp	0.0K	0.0K	103.6Kbp	0.2Mbp
MEGAHIT-MetaBAT2-group-0.15	194.9Kbp	194.9Kbp	0.0K	0.0K	194.9Kbp	0.2Mbp
MEGAHIT-MetaBAT2-group-0.16	1.9Kbp	1.7Kbp	0.4K	0.7K	14.1Kbp	2.1Mbp
MEGAHIT-MetaBAT2-group-0.17	166.6Kbp	43.1Kbp	0.0K	0.0K	166.6Kbp	0.3Mbp
MEGAHIT-MetaBAT2-group-0.18	431.2Kbp	425.9Kbp	0.0K	0.0K	503.4Kbp	1.4Mbp
MEGAHIT-MetaBAT2-group-0.19	151.5Kbp	99.6Kbp	0.0K	0.0K	306.6Kbp	5.3Mbp
MEGAHIT-MetaBAT2-group-0.20	176.2Kbp	96.3Kbp	0.0K	0.0K	270.0Kbp	3.5Mbp
MEGAHIT-MetaBAT2-group-0.21	279.1Kbp	108.3Kbp	0.0K	0.0K	633.9Kbp	4.8Mbp
MEGAHIT-MetaBAT2-group-0.23	50.4Kbp	12.4Kbp	0.0K	0.0K	88.9Kbp	0.3Mbp
MEGAHIT-MetaBAT2-group-0.24	9.3Kbp	6.0Kbp	0.0K	0.0K	17.1Kbp	0.2Mbp
MEGAHIT-MetaBAT2-group-0.25	82.4Kbp	40.3Kbp	0.0K	0.0K	227.2Kbp	1.2Mbp
MEGAHIT-MetaBAT2-group-0.26	614.3Kbp	614.3Kbp	0.0K	0.0K	614.3Kbp	0.6Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.1	186.2Kbp	186.2Kbp	0.0K	0.0K	186.2Kbp	0.2Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.2	90.2Kbp	90.2Kbp	0.0K	0.0K	90.2Kbp	0.1Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.3	67.1Kbp	67.1Kbp	0.0K	0.0K	67.1Kbp	0.1Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.4	66.7Kbp	66.7Kbp	0.0K	0.0K	66.7Kbp	0.1Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.5	58.8Kbp	58.8Kbp	0.0K	0.0K	58.8Kbp	0.1Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.6	40.9Kbp	40.9Kbp	0.0K	0.0K	40.9Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.7	33.1Kbp	33.1Kbp	0.0K	0.0K	33.1Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.8	31.8Kbp	31.8Kbp	0.0K	0.0K	31.8Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.9	31.8Kbp	31.8Kbp	0.0K	0.0K	31.8Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.10	31.7Kbp	31.7Kbp	0.0K	0.0K	31.7Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.11	31.6Kbp	31.6Kbp	0.0K	0.0K	31.6Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.12	30.6Kbp	30.6Kbp	0.0K	0.0K	30.6Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.13	30.0Kbp	30.0Kbp	0.0K	0.0K	30.0Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.14	29.5Kbp	29.5Kbp	0.0K	0.0K	29.5Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.15	29.2Kbp	29.2Kbp	0.0K	0.0K	29.2Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.16	27.0Kbp	27.0Kbp	0.0K	0.0K	27.0Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.17	26.4Kbp	26.4Kbp	0.0K	0.0K	26.4Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.18	25.1Kbp	25.1Kbp	0.0K	0.0K	25.1Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.19	24.6Kbp	24.6Kbp	0.0K	0.0K	24.6Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.20	24.0Kbp	24.0Kbp	0.0K	0.0K	24.0Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.21	23.8Kbp	23.8Kbp	0.0K	0.0K	23.8Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.22	23.5Kbp	23.5Kbp	0.0K	0.0K	23.5Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.23	21.5Kbp	21.5Kbp	0.0K	0.0K	21.5Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.24	21.5Kbp	21.5Kbp	0.0K	0.0K	21.5Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.25	21.4Kbp	21.4Kbp	0.0K	0.0K	21.4Kbp	0.0Mbp
MEGAHIT-MetaBAT2-group-0.unbinned.26	20.2Kbp	20.2Kbp	0.0K	0.0K	20.2Kbp	0.0Mbp

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: quast_table-1_table

Sort	Column	Description	ID
\|\|	N50 (Kbp)	N50 is the contig length such that using longer or equal length contigs produces half (50%) of the bases of the assembly.	`quast-N50`
\|\|	N75 (Kbp)	N75 is the contig length such that using longer or equal length contigs produces 75% of the bases of the assembly	`quast-N75`
\|\|	L50 (K)	L50 is the number of contigs larger than N50, i.e. the minimum number of contigs comprising 50% of the total assembly length.	`quast-L50`
\|\|	L75 (K)	L75 is the number of contigs larger than N75, i.e. the minimum number of contigs comprising 75% of the total assembly length.	`quast-L75`
\|\|	Largest contig (Kbp)	The size of the largest contig of the assembly	`quast-Largest_contig`
\|\|	Length (Mbp)	The total number of bases in the assembly.	`quast-Total_length`

Number of Contigs

This plot shows the number of contigs found for each assembly, broken down by length.

Created with MultiQC

CheckM

Estimates genome completeness and contamination based on the presence or absence of marker genes.URL: https://github.com/Ecogenomics/CheckMDOI: 10.1101/gr.186072.114

Bin quality

The quality of microbial genomes recovered from isolates, single cells, and metagenomes.

An automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes.

Showing ⁸⁵/₈₅ rows and ⁶/₆ columns.

Bin Id	Marker lineage	Genomes	Markers	Marker sets	Completeness	Contamination
MEGAHIT-CONCOCT-group-0_0	root (UID1)	5656	56	24	8.33%	0.00%
MEGAHIT-CONCOCT-group-0_2	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_3	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_4	k__Bacteria (UID203)	5449	104	58	51.32%	16.80%
MEGAHIT-CONCOCT-group-0_5	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_6	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_7	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_10	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_11	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_12	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_13	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_14	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_15	o__Lactobacillales (UID462)	85	367	162	99.07%	2.78%
MEGAHIT-CONCOCT-group-0_16	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_17	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_18	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_19	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_20	k__Archaea (UID2)	207	149	107	39.44%	18.67%
MEGAHIT-CONCOCT-group-0_21	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_24	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_25	k__Bacteria (UID203)	5449	104	58	33.19%	5.33%
MEGAHIT-CONCOCT-group-0_26	k__Bacteria (UID203)	5449	103	58	4.42%	0.00%
MEGAHIT-CONCOCT-group-0_27	g__Bacillus (UID865)	36	1200	269	96.44%	3.35%
MEGAHIT-CONCOCT-group-0_28	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_29	f__Enterobacteriaceae (UID5124)	134	1173	336	99.17%	0.10%
MEGAHIT-CONCOCT-group-0_30	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_32	o__Bacillales (UID828)	139	508	174	99.43%	1.18%
MEGAHIT-CONCOCT-group-0_33	f__Enterobacteriaceae (UID5066)	55	546	224	99.97%	1.45%
MEGAHIT-CONCOCT-group-0_34	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_35	k__Archaea (UID2)	207	149	107	4.10%	0.47%
MEGAHIT-CONCOCT-group-0_36	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_37	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_38	k__Bacteria (UID203)	5449	104	58	1.72%	0.00%
MEGAHIT-CONCOCT-group-0_39	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_40	k__Bacteria (UID203)	5449	103	57	14.35%	0.00%
MEGAHIT-CONCOCT-group-0_41	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_44	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_45	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_47	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_48	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_49	k__Archaea (UID2)	207	149	107	0.07%	0.00%
MEGAHIT-CONCOCT-group-0_50	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_51	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_52	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_53	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_54	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_56	o__Lactobacillales (UID374)	471	348	189	99.82%	0.18%
MEGAHIT-CONCOCT-group-0_58	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_59	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_60	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-CONCOCT-group-0_61	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MaxBin2-group-0.001	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MaxBin2-group-0.002	o__Lactobacillales (UID462)	85	367	162	99.07%	2.78%
MEGAHIT-MaxBin2-group-0.006	o__Bacillales (UID828)	139	508	174	99.43%	3.79%
MEGAHIT-MaxBin2-group-0.010	f__Enterobacteriaceae (UID5124)	134	1173	336	99.47%	0.34%
MEGAHIT-MaxBin2-group-0.011	g__Bacillus (UID865)	36	1200	269	93.67%	3.46%
MEGAHIT-MetaBAT2-group-0.3	g__Bacillus (UID865)	36	1200	269	91.52%	2.44%
MEGAHIT-MetaBAT2-group-0.4	o__Lactobacillales (UID374)	471	348	189	98.77%	0.18%
MEGAHIT-MetaBAT2-group-0.9	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.11	o__Bacillales (UID828)	139	508	174	99.43%	1.18%
MEGAHIT-MetaBAT2-group-0.12	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.13	o__Lactobacillales (UID462)	85	367	162	96.60%	2.85%
MEGAHIT-MetaBAT2-group-0.19	f__Enterobacteriaceae (UID5066)	55	546	224	98.66%	1.00%
MEGAHIT-MetaBAT2-group-0.21	f__Enterobacteriaceae (UID5124)	134	1173	336	98.88%	0.10%
MEGAHIT-MetaBAT2-group-0.24	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.1	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.2	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.6	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.8	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.9	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.10	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.11	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.12	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.13	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.14	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.15	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.17	f__Enterobacteriaceae (UID5124)	134	1173	336	0.60%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.18	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.19	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.21	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.22	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.23	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.24	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.25	root (UID1)	5656	56	24	0.00%	0.00%
MEGAHIT-MetaBAT2-group-0.unbinned.26	root (UID1)	5656	56	24	0.00%	0.00%

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: checkm-first-table_table

Sort	Column	Description	ID
\|\|	Marker lineage	indicates lineage used for inferring marker set (a precise indication of where a bin was placed in CheckM's reference tree can be obtained with the tree_qa command)	`Marker_lineage`
\|\|	Genomes	Number of reference genomes used to infer marker set.	`genomes`
\|\|	Markers	Number of inferred marker genes.	`markers`
\|\|	Marker sets	Number of inferred co-located marker sets	`marker_sets`
\|\|	Completeness	Estimated completeness of genome as determined from the presence/absence of marker genes and the expected collocalization of these genes	`Completeness`
\|\|	Contamination	Estimated contamination of genome as determined by the presence of multi-copy marker genes and the expected collocalization of these genes	`Contamination`

Prokka

Rapid annotation of prokaryotic genomes.URL: http://www.vicbioinformatics.com/software.prokka.shtmlDOI: 10.1093/bioinformatics/btu153

This barplot shows the distribution of different types of features found in each contig.

Prokka can detect different features:

CDS
rRNA
tmRNA
tRNA
miscRNA
signal peptides
CRISPR arrays

This barplot shows you the distribution of these different types of features found in each contig.

Created with MultiQC

GTDB-Tk

Assigns objective taxonomic classifications to bacterial and archaeal genomes.URL: https://ecogenomics.github.io/GTDBTk/index.htmlDOI: 10.1093/bioinformatics/btac672

MAG taxonomy

The taxonomy of a MAG as found by GTDB.

GTDB-Tk is a software toolkit for assigning objective taxonomic classifications to bacterial and archaeal genomes based on the Genome Database Taxonomy GTDB. It is designed to work with recent advances that allow hundreds or thousands of metagenome-assembled genomes (MAGs) to be obtained directly from environmental samples. It can also be applied to isolate and single-cell genomes.

Showing ¹⁷/₁₇ rows and ⁶/₈ columns.

User genome	Classification	Full classification	Classification method	ANI to closest genome	AF to closest genome	RED	Notes
MEGAHIT-CONCOCT-group-0_15.fa	s__Lactiplantibacillus plantarum	d__Bacteria; p__Bacillota; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Lactiplantibacillus; s__Lactiplantibacillus plantarum	ani_screen	98.8	0.9		classification based on ANI only
MEGAHIT-CONCOCT-group-0_25.fa	s__	d__Bacteria; p__Bacillota; c__Bacilli; o__Lactobacillales; f__Streptococcaceae; g__Lactococcus; s__	taxonomic classification defined by topology and ANI			1.0
MEGAHIT-CONCOCT-group-0_27.fa	s__Bacillus velezensis	d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales; f__Bacillaceae; g__Bacillus; s__Bacillus velezensis	ani_screen	98.8	1.0		classification based on ANI only
MEGAHIT-CONCOCT-group-0_29.fa	s__Enterobacter hormaechei_C	d__Bacteria; p__Pseudomonadota; c__Gammaproteobacteria; o__Enterobacterales; f__Enterobacteriaceae; g__Enterobacter; s__Enterobacter hormaechei_C	ani_screen	97.0	0.9		classification based on ANI only
MEGAHIT-CONCOCT-group-0_32.fa	s__Priestia zanthoxyli	d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales; f__Bacillaceae_H; g__Priestia; s__Priestia zanthoxyli	ani_screen	96.4	0.9		classification based on ANI only
MEGAHIT-CONCOCT-group-0_33.fa	s__Serratia liquefaciens	d__Bacteria; p__Pseudomonadota; c__Gammaproteobacteria; o__Enterobacterales; f__Enterobacteriaceae; g__Serratia; s__Serratia liquefaciens	ani_screen	98.5	0.9		classification based on ANI only
MEGAHIT-CONCOCT-group-0_56.fa	s__Leuconostoc pseudomesenteroides	d__Bacteria; p__Bacillota; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Leuconostoc; s__Leuconostoc pseudomesenteroides	ani_screen	96.2	0.8		classification based on ANI only
MEGAHIT-MaxBin2-group-0.002.fa	s__Lactiplantibacillus plantarum	d__Bacteria; p__Bacillota; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Lactiplantibacillus; s__Lactiplantibacillus plantarum	ani_screen	98.7	0.9		classification based on ANI only
MEGAHIT-MaxBin2-group-0.006.fa	s__Priestia zanthoxyli	d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales; f__Bacillaceae_H; g__Priestia; s__Priestia zanthoxyli	ani_screen	96.0	0.9		classification based on ANI only
MEGAHIT-MaxBin2-group-0.010.fa	s__Enterobacter hormaechei_C	d__Bacteria; p__Pseudomonadota; c__Gammaproteobacteria; o__Enterobacterales; f__Enterobacteriaceae; g__Enterobacter; s__Enterobacter hormaechei_C	ani_screen	97.0	0.9		classification based on ANI only
MEGAHIT-MaxBin2-group-0.011.fa	s__Bacillus velezensis	d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales; f__Bacillaceae; g__Bacillus; s__Bacillus velezensis	ani_screen	98.8	1.0		classification based on ANI only
MEGAHIT-MetaBAT2-group-0.3.fa	s__Bacillus velezensis	d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales; f__Bacillaceae; g__Bacillus; s__Bacillus velezensis	ani_screen	98.8	1.0		classification based on ANI only
MEGAHIT-MetaBAT2-group-0.4.fa	s__Leuconostoc pseudomesenteroides	d__Bacteria; p__Bacillota; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Leuconostoc; s__Leuconostoc pseudomesenteroides	ani_screen	97.4	0.9		classification based on ANI only
MEGAHIT-MetaBAT2-group-0.11.fa	s__Priestia zanthoxyli	d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales; f__Bacillaceae_H; g__Priestia; s__Priestia zanthoxyli	ani_screen	96.4	0.9		classification based on ANI only
MEGAHIT-MetaBAT2-group-0.13.fa	s__Lactiplantibacillus plantarum	d__Bacteria; p__Bacillota; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Lactiplantibacillus; s__Lactiplantibacillus plantarum	ani_screen	98.8	0.9		classification based on ANI only
MEGAHIT-MetaBAT2-group-0.19.fa	s__Serratia liquefaciens	d__Bacteria; p__Pseudomonadota; c__Gammaproteobacteria; o__Enterobacterales; f__Enterobacteriaceae; g__Serratia; s__Serratia liquefaciens	ani_screen	98.6	0.9		classification based on ANI only
MEGAHIT-MetaBAT2-group-0.21.fa	s__Enterobacter hormaechei_C	d__Bacteria; p__Pseudomonadota; c__Gammaproteobacteria; o__Enterobacterales; f__Enterobacteriaceae; g__Enterobacter; s__Enterobacter hormaechei_C	ani_screen	97.0	0.9		classification based on ANI only

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order. Table ID: gtdbtk-first-table_table

Sort	Column	Description	ID
\|\|	Classification	GTDB taxonomy string inferred by the GTDB-Tk.	`classification`
\|\|	Full classification	Full GTDB taxonomy string inferred by the GTDB-Tk.	`full_classification`
\|\|	Classification method	Indicates the rule used to classify the genome.	`classification_method`
\|\|	ANI to closest genome	Depending on the classification method, either the 'closest_genome_ani' or 'closest_placement_ani'.	`ANI`
\|\|	AF to closest genome	Depending on the classification method, either the 'closest_genome_af' or 'closest_placement_af'.	`AF`
\|\|	RED	Indicates the relative evolutionary divergence (RED) for a query genome. RED is not calculated when a query genome can be classified based on ANI.	`red_value`
\|\|	Warnings	Indicates unusual characteristics of the query genome that may impact the taxonomic assignment.	`warnings`
\|\|	Notes	Provides additional information regarding the classification of the genome.	`note`

Software Versions

Software Versions lists versions of software tools extracted from file contents.

Group	Software	Version
ADJUST_MAXBIN2_EXT	coreutils	`9.5`
BIN_SUMMARY	pandas	`1.4.3`
	python	`3.10.6`
BOWTIE2_ASSEMBLY_ALIGN	bowtie2	`2.4.2`
	pigz	`2.3.4`
	samtools	`1.11`
BOWTIE2_ASSEMBLY_BUILD	bowtie2	`2.4.2`
BOWTIE2_HOST_REMOVAL_ALIGN	bowtie2	`2.4.2`
BOWTIE2_HOST_REMOVAL_BUILD	bowtie2	`2.4.2`
CHECKM_LINEAGEWF	checkm	`1.2.3`
CHECKM_QA	checkm	`1.2.3`
CONCAT_BINQC_TSV	csvtk	`0.31.0`
CONCOCT_CONCOCT	concoct	`1.1.0`
CONCOCT_CONCOCTCOVERAGETABLE	concoct	`1.1.0`
CONCOCT_CUTUPFASTA	concoct	`1.1.0`
CONCOCT_EXTRACTFASTABINS	concoct	`1.1.0`
CONCOCT_MERGECUTUPCLUSTERING	concoct	`1.1.0`
CONVERT_DEPTHS	bioawk	`20110810`
DASTOOL_DASTOOL	dastool	`1.1.7`
DASTOOL_FASTATOCONTIG2BIN_CONCOCT	dastool	`1.1.7`
DASTOOL_FASTATOCONTIG2BIN_MAXBIN2	dastool	`1.1.7`
DASTOOL_FASTATOCONTIG2BIN_METABAT2	dastool	`1.1.7`
DASTOOL_FASTATOCONTIG2BIN_TIARA	dastool	`1.1.7`
FASTQC_RAW	fastqc	`0.12.1`
FASTQC_TRIMMED	fastqc	`0.12.1`
GENOMAD_ENDTOEND	genomad	`1.11.0`
GTDBTK_CLASSIFYWF	gtdb_db	`r226`
	gtdbtk	`2.5.2`
GTDBTK_SUMMARY	pandas	`1.4.3`
	python	`3.10.6`
GUNZIP_BINS	gunzip	`1.13`
GUNZIP_SHORTREAD_ASSEMBLIES	gunzip	`1.13`
GUNZIP_UNBINS	gunzip	`1.13`
MAG_DEPTHS	pandas	`1.1.5`
	python	`3.6.7`
MAG_DEPTHS_PLOT	pandas	`1.3.0`
	python	`3.9.6`
	seaborn	`0.11.0`
MAG_DEPTHS_SUMMARY	pandas	`1.4.3`
	python	`3.10.6`
MAXBIN2	maxbin2	`2.2.7`
MEGAHIT	megahit	`1.2.9`
METABAT2_JGISUMMARIZEBAMCONTIGDEPTHS_SHORTREAD	metabat2	`2.15`
METABAT2_METABAT2	metabat2	`2.17`
PRODIGAL	pigz	`2.6`
	prodigal	`2.6.3`
Prokka	prokka	`1.14.6`
QUAST	metaquast	`5.0.2`
	python	`3.7.6`
QUAST_BINS	metaquast	`5.0.2`
	python	`3.7.6`
QUAST_BINS_SUMMARY	cp	`9.5`
RENAME_POSTDASTOOL	coreutils	`9.5`
RENAME_PREDASTOOL	coreutils	`9.5`
SEQKIT_STATS	seqkit	`2.9.0`
SPLIT_FASTA	biopython	`1.7.4`
	pandas	`1.1.5`
	python	`3.6.7`
TIARA_SUMMARY	csvtk	`0.31.0`
TIARA_TIARA	tiara	`1.0.3`
Workflow	Nextflow	`25.04.8`
	nf-core/mag	`v5.0.0-g3d41222`
fastp	fastp	`0.24.0`
r-base	r-base	`4.1.3`
r-tidyverse	r-tidyverse	`1.3.1`

nf-core/mag Methods Description

Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/nf-core/mag

Methods

Data was processed using nf-core/mag v5.0.0 ((doi: 10.1093/nargab/lqac007); Krakau et al., 2022) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

The pipeline was executed with Nextflow v25.04.8 (Di Tommaso et al., 2017) with the following command:

nextflow run 'https://github.com/nf-core/mag' -name anna_mags_coffee_v1_7 -params-file 'https://api.cloud.seqera.io/ephemeral/fwGUp9NyvRE4iM6BV2F-hQ.json' -with-tower -r 3d41222776d2ed7a78dd3fd4dd643690c49f6bd2 -resume 650b5bc1-af6c-4164-b72a-02c5f895245b

References

Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
Krakau, S., Straub, D., Gourlé, H., Gabernet, G., & Nahnsen, S. (2022). nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning. NAR Genomics and Bioinformatics, 4(1). https://doi.org/10.1038/s41587-020-0439-x
Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192

Notes:

The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

nf-core/mag Workflow Summary

- this information is collected when the pipeline is started.URL: https://github.com/nf-core/mag

Input/output options

email: annalifousi@gmail.com
input: az://seqera/results/anna-mags/coffee.csv
multiqc_title: MultiQC_coffee_coassembly
outdir: az://seqera/results/anna-mags/results_coffee/

Quality control for short reads options

host_fasta: az://seqera/raw/coffee/coffee_genome/GCF_003713225.1_Cara_1.0_genomic.fna
host_removal_save_ids: true
host_removal_verysensitive: true
keep_phix: true
save_clipped_reads: true
save_hostremoved_reads: true

Quality control for long reads options

longreads_min_quality: 1
skip_adapter_trimming: true
skip_longread_filtering: true
skip_longread_qc: true

Taxonomic profiling options

cat_db: az://seqera/databases/cat/20241212_CAT_nr_website/
gtdb_db: az://seqera/databases/gtdb/GTDB/gtdbtk_package/full_package/release226/
gtdbtk_max_contamination: 10.0
gtdbtk_min_completeness: 30
gtdbtk_use_full_tree: true

Assembly options

coassemble_group: true
skip_flye: true
skip_metamdbg: true
skip_spades: true
skip_spadeshybrid: true

Gene prediction and annotation options

skip_metaeuk: true

Virus identification options

genomad_db: az://seqera/databases/geNomadDB/14886553/genomad_db/
run_virus_identification: true

Binning options

bin_domain_classification: true
bin_max_size: 10000000
min_length_unbinned_contigs: 20000
save_assembly_mapped_reads: true

Bin quality check options

binqc_tool: checkm
busco_db_lineage: N/A
checkm_db: az://seqera/databases/checkm_data/
gunc_save_db: true
refine_bins_dastool: true

Core Nextflow options

configFiles: /.nextflow/assets/nf-core/mag/nextflow.config, /mnt/batch/tasks/workitems/nf-workflow-3PovPb4bn8UU5a/job-1/nf-workflow-3PovPb4bn8UU5a/wd/nextflow.config
launchDir: /mnt/batch/tasks/workitems/nf-workflow-3PovPb4bn8UU5a/job-1/nf-workflow-3PovPb4bn8UU5a/wd
profile: standard
projectDir: /.nextflow/assets/nf-core/mag
revision: 5.0.0
runName: anna_mags_coffee_v1_7
userName: root
workDir: /seqera/results/anna-mags/work

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